ReenactGAN: Learning to Reenact Faces via Boundary Transfer

Wayne Wu, Yunxuan Zhang, Cheng Li, Chen Qian, Chen Change Loy; Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 603-619

We present a novel learning-based framework for face reenactment. The proposed method, known as ReenactGAN, is capable of transferring facial movements and expressions from an arbitrary person’s monocular video input to a target person’s video. Instead of performing a direct transfer in the pixel space, which could result in structural artifacts, we first map the source face onto a boundary latent space. A transformer is subsequently used to adapt the source face’s boundary to the target’s boundary. Finally, a target-specific decoder is used to generate the reenacted target face. Thanks to the effective and reliable boundary-based transfer, our method can perform photo-realistic face reenactment. In addition, ReenactGAN is appealing in that the whole reenactment process is purely feed-forward, and thus the reenactment process can run in real-time (30 FPS on one GTX 1080 GPU). Dataset and model are publicly available on our project page.